Brain matching

ABSTRACT

This invention, which focuses on personality and aptitude matching by psychophysiologic response to stimuli, is referred to as Brain Matching. In general terms, this invention starts by selecting highly specialized skill sets and top performer group for each skill set. The various groups are analyzed though psychophysiologic stimuli testing by using basically the same testing consisting of large numbers stimuli tests in a consistent testing environment. Stimuli tests can range from hundreds to thousands of images each producing a brainwave response. Neural Networks, Artificial Intelligence, Deep Learning computers look at the test results, highly specialized group by other highly specialized group to reduce the groups signature/response commonality into a template. Test subjects are then tested using the same stimuli. The subject&#39;s test results are analyzed for correlation with the various specialized expert groups.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit to and incorporates by reference U.S.provisional patent application No. 62/310,542 filed on Mar. 18, 2016.

This application incorporates by reference the following U.S patent andpatent applications: U.S. Pat. No. 8,684,926B2, US20150164363A1, andUS20140163408A1.

BACKGROUND

The field of this invention relates to electronic brain monitoringtechniques.

We spend a significant amount of time and money trying to determine “whoam I?”, “what do I want to be?” and “what am I naturally good at.” Oneof the basic questions to a child or young adult is “What do you want tobe when you grow up?” Their initial response may reflect somethingexciting as a fireman, policeman, or sports star. Others may take a morehuman approach of being a nurse, doctor, or veterinarian. Many times,the basis for their decision is on something they saw on TV, internet orheard from their peers. Others are influenced by their parents' wishesor a school teacher's guidance. This “who do I want to be?” questiontakes a more serious course when a young high school student starts toelect specialized courses to focus on college. A high school student'scollege selection decisions will have significant impact on the rest ofhis/her life. Once in college, the average student changes their majormore than twice. People will normally be happy doing things that comeeasy to them. One risk is that people don't find out what they want todo until years down the road.

Employers spend a lot of time and money searching for young collegegraduates to train to become professionals. Yet a large number ofpersonnel quit for something else after years of investment. A classicexample is the U.S. military, who spend billions of dollars to attractskilled individuals. The military recruits for basic and advancedtraining and then invests significantly more money in specializedtraining for military gunners, drivers, pilots, computer operations,weapon specialists, etc. Finding personnel to train for highlyspecialized positions such as fighter pilots, special operationspersonnel, and specialized physicians is an especially expensive andtime consuming process.

Many persons may have strong skills yet are not aware because they maynot have been exposed the areas where they have strength. An examplewould be a young adult that never played an instrument but has aninherent ability to do well in music if exposed. The problem is how toidentify hidden skills in a person that has the ability to be great in aparticular profession but is not aware of this since he was neverexposed to the profession.

Administration of standardized tests such as the Myers-Briggs or similartests measuring knowledge, personality traits, or cognitive abilityrequires a substantial amount of time for the candidate to read orlisten to questions and record responses on paper or electronic media.Such tests can be compromised by the self-reporting biases of thecandidate being tested. The candidate has an opportunity to consider thequestion and shape a response suited to how the candidate wishes to beperceived rather than providing the strictly objective response.

Tests based on written or spoken stimuli can be limited in their abilityto probe the full spectrum of the psyche of the candidate. Conventionaltests can also limit the responses to stimuli to very simplistic binaryanswers or multiple choice answers recorded by pencil, paper, orelectronic means. Interpretation of test results requires subjectiveassessments of skilled personnel. Consequently, conventional testing topredict the suitability of persons to perform particular functions hasoften not proven to be reliable due to the subjective nature of theassessment.

Conventional personality type indicators classify persons in a relativesmall number of specific categories. For example Myers-Briggs classifiesa person in 1 of 16 categories. Thus conventional personality typeindicators may not have the fidelity necessary to capture traits thatare indicative of certain subgroups of the human population, such ascertain high performing personnel.

DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 illustrates a block diagram of the steps used to select thestimuli to develop the common test in conjunction with selecting onedesired skill set and a group of high performing of individuals then theprocess utilizing neural networks to develop a signature/template.

FIG. 2 illustrates a block diagram of the steps taken to repeat part ofthe process shown in FIG. 1 selecting a second skill set but utilizingthe same tests and environments.

FIG. 3 illustrates a block diagram of the steps taken to repeat theprocess in FIG. 2 but this is repeated N times.

FIG. 4 illustrates a block diagram selecting individuals to be tested,tested, compared to special skill set groups and determining anyrelationship of the tested individual to any of the special skillgroups.

FIG. 5 illustrates a block diagram of the steps taken to assess thesuitability of multiple candidates for a single function, according toan implementation of this disclosure.

FIG. 6 illustrates a block diagram of the steps taken to assess thesuitability of multiple candidates for multiple functions, according toan implementation of this disclosure.

FIG. 7 illustrates instrumentation employed is the essential embodimentof the system working on brainwave psychophysiologic response toexternal stimuli, according to an implementation of this disclosure.

FIG. 8 illustrates brainwave sensor locations for 30 sensors, accordingto an implementation of this disclosure.

FIG. 9 illustrates instrumentation employed to produce multiple sensorinputs and multiple sensors used to observe psychophysiologic responseto external stimuli, according to an implementation of this disclosure.

FIG. 10 shows an example computing device according to an implementationof this disclosure.

FIG. 11 shows an example network arrangement according to animplementation of this disclosure.

FIG. 12. Is a chart showing the various scientific designations forbrainwaves' response to stimuli.

DRAWING REFERENCE NUMERALS

-   -   10 Selection of Stimuli    -   11 Selection of Testing Equipment and Testing Environment    -   12 Selection of the Desired Skill Set    -   13 Finding High Performing Individual in the Desired Skill Set    -   14 Administering Stimulus and Brain Wave Test    -   15 Presenting raw test data to a computer Neural Network system        to search for commonality and reduce noise.    -   16 Developing the Desired Skill Set group signature/template    -   21 Common Test and Environment    -   22 Selecting Special Skill Set #2    -   23 Finding High Performing Individuals in the Desired Skill Set    -   24 Administering Stimulus and Brain Wave Test    -   25 Presenting raw test data to a computer Neural Network system        to search for commonality and reduce noise.    -   26 Developing the Desired Skill Set group signature/template    -   31 Common Test and Environment    -   32 Selecting Special Skill Set N    -   33 Finding High Performing Individuals in the Desired Skill Set        N    -   34 Administering Stimulus and Brain Wave Test    -   35 Presenting raw test data to a computer Neural Network system        to search for commonality and reduce noise.    -   36 Developing the Desired Skill Set group signature/template for        Skill Set N    -   41 Common Test and Environment    -   42 Select Individual to be Tested    -   43 Administer Stimulus and Tests to Selected Individual    -   44 Data reduction and producing the Tested individual's brain        template    -   45 Search/Comparison Algorithm that compares the Tested        Individual's template to the Special Sill Sets HPIs templates.    -   46 Determine if Tested Individual matches any Special Skill        Groups templates.    -   101 Identification of High Performing Individuals of a        particular skill set    -   102 Identification of Common Physiological Characteristics    -   103 Identification of Stimuli for testing    -   104 Identification Physical Measures of Response to Stimuli    -   105 Test Stimuli on High Performing Individuals of the same        skill set.    -   106 Determine confidence in match or no match    -   107 Administer Stimuli set to test subjects    -   108 Correlate Candidate Responses    -   109 Refine Stimuli Set    -   110 Candidate Response Match?    -   111 Add to Group of Interest    -   112 Reject    -   201 Group of Interest Stimuli    -   202 Group of Interest Signature    -   203 Stimuli Set Database    -   204 GOI Signature Database    -   205 Administer Stimuli Set to GOI Candidates    -   206 Correlate Candidate Response w/GOI Response    -   207 Report Correlation    -   301 EEG data processing computer    -   302 EEG sensors    -   302 a EEG sensor cable harness    -   303 Interviewee    -   306 Interviewee support structure    -   308 Interviewee input device    -   312 Graphical display device    -   314 Control Computer    -   315 Data and Synchronization cable    -   316 System Support Structure    -   317 Stimuli Database    -   318 GOI Signature Database    -   501 EEG data processing computer    -   502 EEG sensor    -   503 Interviewee    -   504 EKG Sensor    -   505 Respiration Band    -   506 Chair    -   507 Shaker    -   508 Interviewee input device    -   509 EKG data processing computer    -   510 Audio output device    -   511 RF Transmitter/Receiver    -   512 Graphical display device    -   513 Camera    -   514 Control computer    -   515 Data synchronization    -   516 System Support Structure    -   517 Stimuli Database    -   518 GOI Signature Database    -   710 Data bus    -   720 Display    -   730 User Input    -   740 Fixed Storage    -   750 Removable media    -   760 I/O controller    -   770 Memory    -   780 Processor    -   790 Network Interface    -   800 Network    -   810 Client    -   820 Client    -   830 Remote Platform    -   840 Server    -   850 Database    -   860 Data store    -   870 Data store

DETAILED DESCRIPTION

According to an implementation of this disclosure, “Brain Matching”techniques may be employed to perform personality and aptitude matchingby measuring psychophysiologic responses to stimuli. In general terms,highly specialized skill sets may be selected along with expert groupsfor each skill set. The various groups may be analyzed thoughpsychophysiologic stimuli testing by using a substantially standardizedtest of large numbers of stimuli in a consistent testing environment.Standardized stimuli tests can include hundreds to thousands of imageseach of which may generally produce a brainwave response in a testsubject. Machine learning techniques employing deep neural networksand/or other techniques driven by artificial intelligence may analyzethe test results.

In an implementation of this disclosure, responses from test subjects ina highly specialized expert group to a standardized test stimuli may becompared using deep neural network techniques to responses from othertest subjects of that expert group to identify a combined signature orother response commonality for that type of group. Response signaturesor commonalities may be stored as template for that expert group. In asimilar way, templates for various other specialized expert groups maybe determined based on their response to the same standardized teststimuli. These templates may be compiled into a set of expert grouptemplates New test subjects may then be tested using the standardizedtest stimuli. The results of the new test subjects may be analyzed forcorrelation with the set of expert group templates. Subjects with astrong correlation to a specific expert group template may be determinedto have a significant probably of performing well in the specializedarea associated with the specific expert group associated with thatspecific template.

Additional benefits of Brain Matching in accordance with animplementation of this disclosure, may be that a person turned away fromthe initial group could be guided to areas where he/she is perhapsbetter suited to mature professionally using the knowledge that he/sheresponds to stimuli in a manner similar to another specialized expertgroup.

Implementations of this disclosure may solve the long-standing problemof identifying candidates that are well suited to perform a particularfunction of interest. This can be accomplished by matching thepsychophysiologic response of a candidate exposed to a set of sensiblestimuli with the psychophysiologic response characteristic of apopulation of persons skilled at performing the function of interestexposed to the same set of sensible stimuli. In one implementation, thepsychophysiologic response may be observed by sensing a variety ofbrainwaves resulting from graphical stimuli. The system and process iscapable of also presenting stimuli using any of the five human sensesand observing psychophysiologic responses such as brainwaves, pupillaryresponse, eye movement, heart rate, heart rate variability, respiration,electrodermal activity, and other responses well known in the art.

Technical literature is replete with examples of distinctive differencesin the personality traits of particular groups of professionals (e.g.,surgeons, astronauts, pilots) compared to the general population.Examples include: “How Do Astronaut Candidate Profiles Differ fromAviation Airline Pilots?;” Aviation Psychology and Applied Human Factors2011; Vol. 1(1):38-44; “Personality as a Predictor of ProfessionalBehavior in Dental School;” Journal of Dental Education; Vol. 69, No.11; 1222; and “A Psychological Profile of Surgeons and SurgicalResidents,” Journal of Surgical Education; Volume 67/Number 6, 359-370.

In “The Warfighters of Today: Personality and Cognitive Characteristicsof Rated Fighter Pilots in the United States Air Force,” Florida StateUniversity Libraries Electronic Theses, Treatises and Dissertations,2010, the author demonstrates that the fighter pilots as a group havedistinctively different scores on the Revised NEO Personality Inventory(NEO PI-R) test compared to the general population as illustrated inFIG. 6.

Implementations of this disclosure may facilitate the assessment ofsuitability for a particular job or task that spans the range from anindividual applying for a single position open for a particular employerto many thousands of people trying to identify which of many positionsthey might be suited (e.g., military occupational specialty).Implementations of this disclosure can also be used for persons toexplore vocations they are suited for, so that they can pursueappropriate fields of study to prepare them for entry or transition inthe work force.

One problem with any new data stream can be knowing how to make sense ofit, understand the information it contains, and exploit the informationfor some purpose. Brainwaves can be characterized as time-varyingvoltages that are caused by neural activity and measured with an arrayof sensors in contact with the scalp. In an implementation of thedisclosures, pattern matching may be employed to identify individualswhose brain responses to certain stimuli are similar to that ofindividuals who are very successful or talented in particular fields. Asan example, if a young person's brain wave signature response to stimuliis similar to an expert aircraft pilot, then it may be expected that theyoung person might also, with training, become an excellent pilot.

Brain waves may include weak signals having a significant quantity ofnoise. Implementations of the disclosure may take an exploratoryapproach, that can identify correlations in brain wave data andextrapolate patterns based on machine learning techniques employing deepneural networks and/or other artificial intelligence techniques.

In implementations of this disclosure machine learning techniques may bebased on statistical classification or computational neural nets(inspired by but not to be confused with biological neural nets such asthe human brain). These machine learning techniques can enable the useof many different inputs without regard to a user's ignorance as towhich inputs are important or even having a concept of what the inputsrepresent. In the case of a neural network such as a multi-layerperceptron, a large number of inputs can be used including those used tocharacterize the stimuli, the brain waves of the person being measured,and temporal delays used to model the brain's latency. As the networkoperates, weights on processing nodes may be adjusted nonlinearly usingalgorithmic feedback known as back-propagation. Over time and manyempirical examples in a training data set, the input nodes that areunimportant to pattern classification can have their weights adjustedtowards zero while those that are significant can have weights thatincrease. In this way, the neural network can “learn” (through weightadjustment) different patterns such as the brain wave patterns ofexemplar humans who represent the best, most successful, and mosttalented individuals in particular domains (or as described above,expert groups). These different patterns can be expressed as a vector ofthe outputs of the neural network, but they can be quite recognizableand characteristic of the various exemplar humans. Thus, after training,the neural network can now classify new persons as having brains thatrespond most similarly to one of the exemplars (or expert group templateas discussed above). One obvious use for such a neural network may be toidentify good fields of endeavor to suggest to young people. If a youngperson's brain wave response to certain stimuli is similar to anexemplar individual in a particular field, then it may be likely thatthe young person's brain is predisposed enable success in that field.

A statistical classifier can be equivalent to a computational neural netfor pattern recognition. Thus, implementations of this disclosure mayemploy techniques in addition to neural networks, such as similarmachine learning methods, or other artificial intelligence driventechniques.

The inputs to the machine learning techniques discussed herein caninclude the brain waves of a test subject who is responding to certainstimuli. Brain waves can vary by frequency and amplitude as well as therates of change in frequency and amplitude based on changes in stimuli.Furthermore, in addition to brain waves, other types ofpsychophysiologic responses may be analyzed including but not limited topupillary response, eye movement, heart rate, heart rate variability,respiration, and electrodermal activity. All of these factors can beinputs to the machine learning system because they are potentiallycorrelated to brain response. For example, brain wave frequency can becorrelated to state-of-mind, computational load on the brain, andcertain personality characteristics such as the degree ofextroversion/introversion.

A computing device may execute various procedures for determining abrainwave signature or template for an expert group of high performingindividuals, according to implementations of this disclosure. Forexample, FIG. 1 shows an example procedure, where at 10 stimuli may beselected to be used as standardized stimuli for all individuals testedfor all skill sets. This test may consist of a significant number ofstimuli such as hundreds or thousands photos of various subjects,numbers, letters, objects, faces, abstract art, geometrical shapes, or3-D presentations. An average human brain can process 12 frames orpictures a second. At this speed, it is hard to recognize photosconsciously but the human brain functions subconsciously at a fasterrate and generates measurable brain response activity to variousstimuli. With the brain processing 100 to 500+ photos a minute,thousands of diverse photos can be used. Photos may be selected thathave a bold subject and solicit a strong response.

In implementations of this disclosure, once the standardized stimuliselection has been made, the common test equipment and testingenvironment 11 can be selected. Since a test goal may be to measurevariations between individuals, the test setup may be configured toreduce as many variables as possible.

As discussed above, one benefit of implementations of this disclosuremay be to determine if unknown persons are mentally wired like highperforming individuals. The first step may be to select the sought afterskill set at 12 and then identify the high performing individuals inthis area at 13. For example, a first set of subjects may be a set ofhigh performing individuals with respect to a sought after skill and thefirst selection criteria may be the sought after skill determined at 12.In some implementations a second set of subjects may be a randomlysampled set of persons selected from the general population or a relatedbaseline set of test subjects. The second selection criteria may be thatthe second set of subjects are randomly selected or otherwise selectedin a manner that results in a suitable baseline of personnel.

Once test subjects are identified, they may be presented with thestandard stimuli at 14. For example, a sensory presentation device, suchas a video screen or projection system may be communicatively connectedto a computing device. The sensory presentation device may present thefirst sequence of stimuli from the standardized stimuli to the set ofhigh performing individuals. In some implementations, the sensorypresentation device may also present the first sequence of stimuli tothe second set of subjects.

During or after presentation of the standardized stimuli, one or moreelectrodes operatively connected to each of the high performingindividuals and in communication with the computing device may detect afirst set of one or more voltage fluctuation sequences from each of thehigh performing individuals. In some implementations, during or afterpresentation of the standardized stimuli, one or more electrodesoperatively connected to each of the second set of subjects and incommunication with the computing device may detect a second set of oneor more voltage fluctuation sequences from each of the second set ofsubjects.

Once the high performing individuals complete the test or as theycomplete the test, their raw test data can be submitted at 15 to acomputing device implementing machine learning techniques that can lookfor commonality in brainwave data among the subjects of the expertgroup. For example, a neural network executing on the first computingdevice may determine a pattern of voltage fluctuations that arecharacteristic of the first set of voltage fluctuations. Thischaracteristic pattern may be stored as a template and associated withthe high performing individuals at 16. In some implementations, theneural network may determine a pattern of voltage fluctuations that arecharacteristic of the first set of voltage fluctuation sequences and notcharacteristic of the second set of voltage fluctuation sequences. Thisdetermined pattern that is not characteristic of the second set ofvoltage fluctuation sequences may be selected as the characteristicpattern for the high performing individuals and stored as a template.

In some implementations, machine learning techniques such as neuralnetworks may execute on computing devices such as one or more remoteservers executing in a cloud computing environment in communication witha local computing device and/or sensory presentation device as discussedherein.

In some implementations, the procedure discussed with respect to FIG. 1may include providing, by the first computing device, a recommendationfor a selection of subjects from among a third set of subjects based onthe pattern of voltage fluctuations. For example, a third set ofsubjects may be presented with the standardized stimuli and theirbrainwave responses to the stimuli may be compared to the storedtemplate associated with the high performing individuals. The brainwaveresponses of a subset of subjects within the third set of subjects maybe determined to exhibit a correlation with the template that exceeds athreshold value. In response to this determination, this subset of thethird set of subjects may be recommended for consideration forperforming the sough after skill associated with the high performingindividuals. In some implementations, recommendations as discussedherein may be provided to other systems such as employee recruitmentsystems or components of enterprise human resources information systemsand serve as a basis for further functionality of those systems. In someimplementations, recommendations may be provided to an interface for auser of a computing device.

FIG. 2 shows a similar process for identifying a new skill set, 22 thenselecting the top performers with that skill set, 23. The same stimulitest and conditions, 21 may be administered, 24 to assist in identifyinga test subject with a better fit into another desired skill area.Similarly, to FIG. 1 neural network processing, 25 may be performed onthe group of experts' raw test data to determine a template or signatureof the group. A signature or template could be made by looking at anumber of skill experts in the desired skill set area since the testdata results are very large datafiles and can be at the giga- orterabyte levels. Neural networks are designed for large data and highnumber of comparisons. At least 10 high-performing individuals should beused to develop a template. Hundreds of HPIs would be statically betterif possible for developing a template or signature.

FIG. 3 expands FIG. 2 with basically the same process but selecting Ndesired skill sets, 32. The more desired skill sets and high performingindividuals tested, the better the signature/template can be derived.FIG. 4 focuses on the unknown individual, 42 to take the standard testto determine if he has a strong match to any of the high performingindividuals group signatures, 46. The process is similar to FIG. 3 butinstead of testing high performing individuals, the testing may beadministered to unknown skill set persons. The goal is to see if theunknown person is a fit to one of the desired skill set group.

In FIG. 5, several steps are illustrated that enable the objectiveprediction of suitability of a candidate to a particular task ororganizational function. The first step, 1 01, of the process mayidentify the high performing individuals (HPI) in a group of interest(GOI) that performs a particular task or organizational function. Instep 1 02, the HPI can be evaluated to identify the common psychologicalcharacteristics or traits which are distinctive to the HPI of the GOI.

In step 1 03, psychological stimuli may be identified which will resultin physiologic responses which can be observed by the sensors of thesystem, 1 04. These stimuli could be any of those that affect the fivehuman senses; sight, hearing, touch, taste, smell. Stimuli of step 1 03and the physical measures of step 1 04 may be evaluated as effectivepredictive indicators of suitability for a particular task or functionby testing people from the general population and HPI. Thepsychophysiologic response of the HPI to the stimuli set is compared tothat of the general population in step 1 06.

An example of how stimuli elicit psychophysiologic responses which areindicative of personality traits is an electronic administration ofsomething like the Big Five Personality Test, which poses severalstatements to a test subject and asks the test subject to indicate howstrongly the statement accurately portrays reflects the test subject.For instance, the test may state that the test subject tends to findfault with others. The test subject responds by filling coloring one offive circles that represent degrees of agreement from “StronglyDisagree” to Strongly Agree.” Asking a test subject the same questionsvia text, visual representation or speech and monitoring the brainwaveresponse by EEG sensors. The N-400 brainwaves may be event-drivenpsychophysiologic responses triggered by external stimuli that challengethe test subject with agreement or disagreement with self-concept of thetest subject. When instructed to assess how well statements describe thetest subject, the amplitude of the N-400 may be proportional to thedegree of agreement with the statement without the test subject havingto indicate their answer by coloring bubbles on a paper form.

FIG. 12 is a Table listing several brainwave responses to variousstimuli. For instance, the P-300 brainwave has proven very effective atindicating a test subject's level of recognition of sounds, words,numbers or images. Appropriate stimuli can be generated and presented tothe test subject and responses recorded. Because the purpose of thissystem is to establish characteristic patterns of response to stimuli,the exact stimuli need not be limited to elucidating personality traitsalone.

In all cases, the response to stimuli may result in a set of measuredvalues with fixed and known ranges. One example is measured voltage froma brain wave as measured by an electrode placed at a particular locationon the scalp. To classify a response, a set of these measured values inaddition to a digital description of the particular stimuli can be inputinto a classifier such as a neural net (e.g., a multi-layer perceptronusing back-propagation during training) or equivalent other classifieralgorithm. The output may be a vector of values that characterize agroup such as individuals who perform well, are experts in, or aretalented in a particular field. This vector of outputs may be a refinedversion of the raw values measured and thus a good, general method ofmeasuring the response to stimuli. The classifier described in thisparagraph is a common component in all embodiments.

If the response of the HPI is distinctly different from that of thegeneral population (e.g., their signature) so that the HPI areidentified to be a HPI of the GOI with a high degree of probability (Pd)and low false alarm rate (Pfar), then the set of stimuli may bevalidated to be reliably predictive and can be administered tocandidates. If not, then the stimuli set may be modified in step 1 09and re-evaluated in steps 1 05 and 1 06 until the stimuli set is deemedsufficiently predictive.

Once the stimuli set is validated as predictive with high Pd and lowPfar, it can be administered to candidates for the GOI in step 1 07. Thepsychophysiologic response of candidates to the stimuli set may becorrelated to that of the HPI response to the same stimuli set. Thestrength of the correlation predicts how well the candidate matches theresponse of the HPI and thus probability that the candidate will also bea strong performer in the GOI; step 1 10. If the strength of matchexceeds a threshold value, the candidate may be deemed to be a fit inthe GOI, step 1 11. If not, the candidate i may be deemed unlikely tofit in the GOI.

FIG. 6 illustrates an extension of the process and system of describedin FIG. 5. The process in FIG. 2 evaluates the degree of fit ofcandidates to multiple groups of interest (GOIs). The steps of FIG. 5may be implemented for multiple tasks or functions so that a library ofdiagnostic stimuli sets 2 01 is populated in database 2 03. Thedistinctive signatures for HPI of each task 2 02 may populate asignature database, 2 04. The library of stimuli data sets may beadministered to candidates for the corresponding GOIs in step 2 05. Theresponse of the candidate to the stimuli may be correlated to those ofthe signatures characteristic of the HPIs of the GOIs in step 2 06.Closeness of fit of the candidate to the GOIs represented may betabulated in a report 2 07.

FIG. 7 illustrates an embodiment of this invention. An interviewee 3 03is seated before a graphical display device, 3 12. In this particularembodiment, stimuli 3 17 may be graphical in nature and are displayed onthe graphical display device, 3 12. Stimuli elements, for instance stillimages, may be displayed at fixed intervals for fixed durations of timein the method of rapid serial visual presentation (RSVP) which is wellknown in the art. Graphical presentation of stimuli by RSVP typicallydisplays images at a rate of 5 to 10 images per second.

One or more sensors 3 02 may be arranged on the test subject's head inlocations according to locations illustrated in FIG. 4 for a 30 channelsystem, in an embodiment. The number and location of channels may differupon the stimulus presented to the interviewee 3 03. The sensors anddata collection and processing collectively facilitateelectroencephalography (EEG). Sensor locations may be selected to obtainstrong signals for specific brainwaves resulting from the RSVP stimuli.Brainwave signals may have characteristic shape, polarity and latencywhich is well established in the art. FIG. 12 Table presents well knownbrainwave signals, their polarity, latency, evoking stimuli andinterpretation.

Communication means 3 15 may provide a channel for data to betransferred between EEG data processing computer 3 01 and controlcomputer 3 14. Channel 3 15 may also provide the timing data needed forEEG data processing computer to know when stimuli is presented to theinterviewee 3 03 so that brainwave latency can be computed. This channelmay be a wired or wireless connection, and may use any data format orprotocol known in the art.

Interviewee input device 3 08 may be used to keep the interviewee 3 03attentive to the graphical display device 3 12 while RSVP of thestimulus data is in progress. For instance, the interviewee may be askedto indicate the display of a particular image by pressing on a keyboardor activating a switch. Input device 3 08 may also be used to measureinterviewee response time, motion inhibition response and similarpsychophysiologic responses.

FIG. 9 illustrates a system in which stimuli may be delivered tomultiple human senses and multiple sensor types are employed to observepsychophysiologic response to the multi-modal stimulation. Stimulusgenerating components may include the audio output device 5 10 andshaker 5 07 which is capable of imparting signals affecting the sense oftouch of the interviewee. For clarity in the figure stimulus generatorsaffecting the senses of taste and smell are not shown but could form apart of this system.

Sensors of the system described in FIG. 9 may include the EEG systemcomponents 5 01 and 5 02; electrocardiogram (EKG) sensors 5 04 and EKGdata processing computer 5 09; respiration band 5 05, RFtransmitter/receiver 5 10, which can be used to measure heart rate,heart rate variability and respiration using RF Doppler vibrometry andelectrodermal activity; and a camera to observe pupillary response, eyemovement and muscle tension. In alternative embodiments, differentsubsets of these sensors may be used.

The system configured in this way can produce one or more sensiblestimuli and monitor one or more psychophysiologic responses to thestimuli.

Description of a Preferred Embodiment

RSVP/EEG for Single GOI

In a preferred embodiment of the invention, the process of FIG. 5 andthe instrumentation of FIG. 7 may be employed to assess the fit ofcandidates for a single GOI.

Operation of Preferred Embodiment

In an embodiment of the invention, the process of FIG. 5 may be employedto establish the characteristic signature response of a subset of aparticular group of interest that is assessed to be high performingindividuals of that group of interest. Through iteration of testing andrefinement, a set of graphical stimuli may be validated to distinguishbetween known members of the group of interest and known non-members ofthe group of interest with a high probability of detection and low falsealarm rate. The validated stimuli set can then be administered toindividuals by RSVP and resulting psychophysiologic response observed byEEG as illustrated in FIG. 7 to determine if they fit the characteristicof the GOI or not. The elements of the stimuli set may be reorderedwithin a stimuli set presentation or mixed amongst the various stimulisets presented.

Embodiment 2

Single non-RSVP input, EEG sensors and Single GOI

In an alternative embodiment of the invention, the process of FIG. 5 andthe instrumentation of FIG. 7 may be employed to assess the fit ofcandidates for a single GOI using stimuli sets evoking psychophysiologicresponse by inputs affecting senses other than the sense of vision.

Operation of Embodiment 2

In this embodiment of the invention, the process of FIG. 5 may beemployed to establish the characteristic signature response of a subsetof a particular group of interest that is assessed to be high performingindividuals of that group of interest. Through iteration of testing andrefinement, a set of graphical stimuli may be validated to distinguishbetween known members of the group of interest and known non-members ofthe group of interest with a high probability of detection and low falsealarm rate. The validated stimuli set may be composed of inputs to asingle human sense other than by sight such as hearing, touch, taste orsmell and is administered to individuals by RSXP where X can be Hearing(H), touch (T), smell (S) or taste (T). The resulting psychophysiologicresponse may be observed by EEG as illustrated in FIG. 7 to determine ifthey fit the characteristic of the GOI or not. The elements of thestimuli set may be reordered within a stimuli set presentation or mixedamongst the various stimuli sets presented.

Embodiment 3

RSVP/EEG for multiple GOI

An alternative embodiment of the invention may be configured to assessthe fit of one or more candidates to more than one GOI by RSVP and EEG.

Operation of Embodiment 3

In this configuration of the invention, the process of FIG. 5 may beemployed to establish the characteristic signature response of HPI foreach of more than one GOIs. For each of more than one GOIs, a set ofgraphical stimuli may be validated to distinguish between known membersof each GOI and known non-members of each GOI with a high probability ofdetection and low false alarm rate. The multiple stimuli sets associatedwith each GOI can then be administered to individuals by RSVP andresulting psychophysiologic response observed by EEG as illustrated inFIG. 7 to determine how well they fit each of the GOIs. The elements ofthe stimuli set may be reordered within a stimuli set presentation ormixed amongst the various stimuli sets presented.

Embodiment 4

RSXP/EEG for multiple GOI

An alternative embodiment of the invention may be configured to assessthe fit of one or more candidates to more than one GOI by RSXP and EEG.

Operation of Embodiment 4

In this configuration of the invention, the process of FIG. 5 may beemployed to establish the characteristic signature response of HPI foreach of more than one GOIs. For each of more than one GOIs, a set ofgraphical stimuli may be validated to distinguish between known membersof each GOI and known non-members of each GOI with a high probability ofdetection and low false alarm rate. The multiple stimuli sets associatedwith each GOI can then be administered to individuals by RSXP andresulting psychophysiologic response observed by EEG as illustrated inFIG. 7 to determine how well they fit each of the GOIs. The elements ofthe stimuli set may be reordered within a stimuli set presentation ormixed amongst the various stimuli sets presented.

Embodiment 5

RSVP, none EEG sensors, Single GOI

An alternative embodiment of the invention, a stimulus set tocharacterize a single GOI may employ RSVP and observations ofpsychophysiologic responses other than brainwaves.

Operation of Embodiment 5

In this embodiment of the invention, the process of FIG. 5 may beemployed to establish the characteristic signature response of a subsetof a particular group of interest that is assessed to be high performingindividuals of a particular group of interest. Through iteration oftesting and refinement, a set of graphical stimuli may be validated todistinguish between known members of the group of interest and knownnon-members of the group of interest with a high probability ofdetection and low false alarm rate. The validated stimuli set can thenbe administered to individuals by RSVP. The resulting psychophysiologicresponse may be observed by instruments other than EEG sensors.Candidate sensors may include one or more cameras sensitive to thevisible and non-visible components of the spectrum (e.g., infrared) tomonitor pupillary response, eye movement, vasodilation, muscle tension,etc.; electrocardiogram for heart rate and heart rate variability;respiration band for respiration rate and abnormalities; RF Dopplervibrometry to observe heart rate, heart rate variability, respirationand muscle movements; skin resistivity measures electrodermal activity.Laser Doppler vibrometry performs the same function as RF DopplerVibrometry. There are many other sensor modes for observingpsychophysiologic responses that are well known in the field ofpolygraphy that could also be employed measurements commonly used inthis embodiment.

Embodiment 6

RSVP, none EEG sensors, Multiple GOIs

An alternative embodiment of the invention, a stimulus set tocharacterize multiple GOIs employs RSVP and observations ofpsychophysiologic responses other than brainwaves.

Embodiment 7

RSXP, none EEG sensors, Single GOI

An alternative embodiment of the invention, a stimulus set tocharacterize a single GOI employs RSXP and observations ofpsychophysiologic responses other than brainwaves.

Embodiment 8

RSXP, none EEG sensors, Multiple GOIs

An alternative embodiment of the invention, a stimulus set tocharacterize multiple GOIs employs RSXP and observations ofpsychophysiologic responses other than brainwaves.

Embodiment 9

RSVP and RSXP, EEG sensors, Single GOI

An alternative embodiment of the invention, a stimulus set tocharacterize a single GOI which may employ a combination of RSVP andRSXP in conjunction with and brainwave observations accomplished by EEGinstrumentation.

Embodiment 10

RSVP and RSXP, EEG sensors, Multiple GOI

An alternative embodiment of the invention, a stimulus set tocharacterize multiple GOIs which may employ a combination of RSVP andRSXP in conjunction with and brainwave observations accomplished by EEGinstrumentation.

Embodiment 11

RSVP and RSXP, EEG and non-EEG sensors, Single GOI

An alternative embodiment of the invention, a stimulus set tocharacterize a single GOI which may employ a combination of RSVP andRSXP in conjunction with EEG and non-EEG observations.

Embodiment 12

RSVP and RSXP, EEG and non-EEG sensors, Multiple GOIs

An alternative embodiment of the invention, a stimulus set tocharacterize multiple GOIs which may employ a combination of RSVP andRSXP in conjunction with EEG and non-EEG observations.

Embodiment 13

Multiple candidates evaluated concurrently for each of the embodimentsabove.

Embodiment 14

An example or embodiment for using the Brain Matching invention may befor military service selection. Before a new recruit makes a decision onbranch of service or which occupational specialty the recruit wish topursue (Infantry, Armor, Logistics, mechanic, etc.), the soldier couldbe told that he is mentally wired like high performers of one or morespecial skill sets. The soldier would then have significantly importantinformation to assist him and the military in investing in costlytraining in an area that does not come easy or enjoyable to him.

Embodiment 15

the brainwave signatures can be kept on file when a soldier enters themilitary. Sometimes soldiers face tremendous mental stress resulting inPost-Traumatic Stress Disorder (PTSD). The soldier that exhibits PTSDcould be retested and compared to his original brainwave reading to seethe degree stimuli responses have changes, possibly indicating theseverity of the PTSD syndrome.

An alternative embodiment of the invention, multiple candidates may beevaluated simultaneously or asynchronously from during a fixed intervalof time. Each candidate may be subjected to the same stimulus sets whichmay be presented in the same or different order.

Embodiment 14

Dynamic selection of stimuli sets.

Candidates can be evaluated by dynamically selected stimuli sets whichare automatically selected by the system based on how well a candidatematches GOIs at high levels of abstraction. For instance, if acandidate's responses match better with a GOI for general engineeringcompared to other vocational types, the system may select stimuli setsfrom a lower tier of engineering disciplines that provide morespecificity in engineering such as mechanical, electrical or software.Levels of specificity for any particular functional category may not belimited.

Embodiment 15

Unlimited Personally Type Indicators

Over time as the Brain Matching invention builds numerous GOI, thesegroups can be assembled to allow a test subject to identify which GOIhe/she would result in the best correlation. This embodiment couldassemble thousands of GOI to provide very specific matching.

Embodiment 16

Synthetic GOIs

This embodiment could allow taking the stimuli results from testsubjects and group the test subjects in groups that currently have notbeen identified. An example is testing numerous candidate personnel who,after testing, do not fit in any GOI. Based on stimuli test results,these subjects may be grouped/pared by stimuli to form their own GOIs.Each GOI could be then examined to see which common skills, interests,and abilities they master.

Implementations of the present disclosure may be implemented in and usedwith a variety of component and network architectures. FIG. 11 is anexample computing device 700, such as a computer, suitable forimplementations of the present disclosure. The computing device 700 mayinclude a bus 710 which interconnects major components of the computingdevice 700, such as a central processor 780; a memory 770 (typicallyRAM, but which may also include ROM, flash RAM, or the like); aninput/output controller 760; a user display 720, such as a displayscreen via a display adapter; a user input interface 730, which mayinclude one or more controllers and associated user input devices suchas a keyboard, mouse, and the like, and may be closely coupled to theI/O controller 760; fixed storage 740, such as a hard drive, flashstorage, Fibre Channel network, SAN device, SCSI device, and the like;and a removable media component 750 operative to control and receive anoptical disk, flash drive, and the like.

The bus 710 may allow data communication between the central processor780 and the memory 770, which may include read-only memory (ROM) orflash memory (neither shown), and random access memory (RAM) (notshown), as previously noted. The RAM may generally be the main memoryinto which the operating system and application programs are loaded. TheROM or flash memory can contain, among other code, the BasicInput-Output system (BIOS) which controls basic hardware operation suchas the interaction with peripheral components. Applications residentwith the computing device 700 may generally be stored on and accessedvia a computing device readable medium, such as a hard disk drive (e.g.,fixed storage 740), an optical drive, floppy disk, or other storagemedium.

The fixed storage 730 may be integral with the computing device 700 ormay be separate and accessed through other interfaces. A networkinterface 790 may provide a direct connection to a remote server via atelephone link, to the Internet via an internet service provider (ISP),or a direct connection to a remote server via a direct network link tothe Internet via a POP (point of presence) or other technique. Thenetwork interface 790 may provide such connection using wirelesstechniques, including digital cellular telephone connection, CellularDigital Packet Data (CDPD) connection, digital satellite data connectionor the like. For example, the network interface 790 may allow thecomputing device to communicate with other computing devices via one ormore local, wide-area, or other networks, as shown in FIG. 12.

Many other devices or components (not shown) may be connected in asimilar manner (e.g., document scanners, digital cameras and so on).Conversely, all of the components shown in FIG. 11 need not be presentto practice the present disclosure. The components can be interconnectedin different ways from that shown. The operation of a computing devicesuch as that shown in FIG. 11 is readily known in the art and is notdiscussed in detail in this application. Code to implement the presentdisclosure can be stored in computing device-readable storage media suchas one or more of the memory 770, fixed storage 740, removable media750, or on a remote storage location.

FIG. 12 shows an example network arrangement according to animplementation of the disclosure. One or more clients 810, 820, such aslocal computing devices, smart phones, tablet computing devices, and thelike may connect to other devices via one or more networks 800. Thenetwork may be a local network, wide-area network, the Internet, or anyother suitable communication network or networks, and may be implementedon any suitable platform including wired and/or wireless networks. Theclients may communicate with one or more servers 840 and/or databases850. The devices may be directly accessible by the clients 810, 820, orone or more other devices may provide intermediary access such as wherea server 840 provides access to resources stored in a database 850. Theclients 810, 820 also may access remote platforms 830 or servicesprovided by remote platforms 830 such as cloud computing arrangementsand services. The remote platform 830 may include one or more servers840 and/or databases 850.

More generally, various implementations of the presently disclosure mayinclude or be implemented in the form of computing device-implementedprocesses and apparatuses for practicing those processes.Implementations also may be implemented in the form of a computingdevice program product having computing device program code containinginstructions implemented in non-transitory and/or tangible media, suchas floppy diskettes, CD-ROMs, hard drives, USB (universal serial bus)drives, or any other machine readable storage medium, wherein, when thecomputing device program code is loaded into and executed by a computingdevice, the computing device becomes an apparatus for practicingimplementations of the disclosure. Implementations also may beimplemented in the form of computing device program code, for example,whether stored in a storage medium, loaded into and/or executed by acomputing device, or transmitted over some transmission medium, such asover electrical wiring or cabling, through fiber optics, or viaelectromagnetic radiation, wherein when the computing device programcode is loaded into and executed by a computing device, the computingdevice becomes an apparatus for practicing implementations of thedisclosure. When implemented on a general-purpose microprocessor, thecomputing device program code segments may configure the microprocessorto create specific logic circuits. In some configurations, a set ofcomputing device-readable instructions stored on a computingdevice-readable storage medium may be implemented by a general-purposeprocessor, which may transform the general-purpose processor or a devicecontaining the general-purpose processor into a special-purpose deviceconfigured to implement or carry out the instructions. Implementationsmay be implemented using hardware that may include a processor, such asa general-purpose microprocessor and/or an Application SpecificIntegrated Circuit (ASIC) that implements all or part of the techniquesaccording to implementations of the disclosure in hardware and/orfirmware. The processor may be coupled to memory, such as RAM, ROM,flash memory, a hard disk or any other device capable of storingelectronic information. The memory may store instructions adapted to beexecuted by the processor to perform the techniques according toimplementations of the disclosure.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific implementations. However, theillustrative discussions above are not intended to be exhaustive or tolimit implementations of the disclosure to the precise forms disclosed.Many modifications and variations are possible in view of the aboveteachings. The implementations were chosen and described in order toexplain the principles of implementations of the disclosure and theirpractical applications, to thereby enable others skilled in the art toutilize those implementations as well as various implementations withvarious modifications as may be suited to the particular usecontemplated.

The invention claimed is:
 1. A method comprising: presenting, by asensory presentation device in communication with a first computingdevice, a first sequence of stimuli to: (i) a first set of subjectsassociated with a first selection criteria and (ii) a second set ofsubjects associated with a second selection criteria; detecting, by anelectrode in communication with the first computing device, a first setof voltage fluctuation sequences, the first set of voltage fluctuationsequences comprising a sequence of voltage fluctuations from each of thefirst set of subjects; detecting, by the electrode, a second set ofvoltage fluctuation sequences, the second set of voltage fluctuationsequences comprising a sequence of voltage fluctuations from each of thesecond set of subjects; determining, by a neural network, a pattern ofvoltage fluctuations characteristic of the first set of voltagefluctuation sequences and not characteristic of the second set voltagefluctuation sequences; and providing, by the first computing device, arecommendation for a selection of subjects from among a third set ofsubjects based on the pattern of voltage fluctuations; presenting, bythe sensory presentation device, a set of sequences of stimuli to thefirst set of subjects; determining, by the neural network, a respectivecorrelation value for each of the set of sequences of stimuli; andselecting, by the first computing device, the first sequence of stimulifrom among the set of sequences of stimuli based on the respectivecorrelation value for the first sequence of stimuli.
 2. The method ofclaim 1, further comprising: presenting, by the sensory presentationdevice, the first sequence of stimuli to the third set of subjects; andselecting, by the first computing device; a fourth set of subjects fromamong the third set of subjects based on the pattern of voltagefluctuations, wherein the provision of the recommendation is based onthe fourth set of subjects.
 3. The method of claim 1, wherein therespective correlation value for the first sequence of stimuli comprisesa measure of interdependence among components of the first sequence ofstimuli and components of a sequence of voltage fluctuations collectedfrom each of the first set of subjects during the presentation of thefirst sequence of stimuli.
 4. The method of claim 1, further comprising:selecting, by the first computing device, the first set of subjectsbased on the first criteria and the second set of subjects based on thesecond criteria.
 5. The method of claim 1, wherein the sensorypresentation device comprises at least one selected from the groupconsisting of: a video display, a speaker, a scent-generating device, atactile generator, and a taste simulation device.
 6. The method of claim1, wherein the presentation of the first sequence of stimuli comprisesrapid serial visual presentation.
 7. The method of claim 1, wherein thefirst sequence of stimuli is unrelated to the first selection criteria.8. The method of claim 1, wherein the first selection criteria comprisesat least one selected from the group consisting of: a test scoreexceeding a threshold score, a membership in an organization, a career,membership in a profession, an accreditation, an academic degree, acertification, a professional title, an amount of experience, an age, agender, a rate of career progression, and a rate of academicprogression.
 9. The method of claim 1, wherein the second set ofsubjects are not associated with the first criteria.
 10. The method ofclaim 1, wherein the second criteria comprises being substantiallyrandomly selected from a population of subjects comprising the first setof subjects and the second set of subjects.
 11. The method of claim 1,wherein the first set of voltage fluctuation sequences and the secondset of voltage fluctuation sequences are collected in accordance withelectroencephalographic techniques.
 12. The method of claim 1, whereinthe electrode is in contact with a scalp of a subject of the first setof subjects.
 13. The method of claim 1, wherein the first set of voltagefluctuation sequences comprise evoked potentials and/or event relatedpotentials.
 14. The method of claim 1, wherein the neural networkcomprises at least one selected from the group consisting of: a deepneural network, convolutional neural network, long short-term memoryneural network, and a convolutional, long short-term memory, fullyconnected deep neural network.
 15. The method of claim 1, wherein theneural network is executing on one or more second computing devicesremote from the first computing device.
 16. The method of claim 1,wherein the neural network is executing on the first computing device.17. The method of claim 1, wherein the pattern of voltage fluctuationscharacteristic of the first set of voltage fluctuation sequencescomprises a pattern of voltage fluctuations more common in the first setof voltage fluctuation sequences than in the second set of voltagefluctuation sequences.
 18. A non-transitory, computer-readable mediumstoring instructions that, when executed by a processor, cause theprocessor to perform operations comprising: presenting a first sequenceof stimuli to: (i) a first set of subjects associated with a firstselection criteria and (ii) a second set of subjects associated with asecond selection criteria; detecting a first set of voltage fluctuationsequences, the first set of voltage fluctuation sequences comprising asequence of voltage fluctuations from each of the first set of subjects;detecting a second set of voltage fluctuation sequences, the second setof voltage fluctuation sequences comprising a sequence of voltagefluctuations from each of the second set of subjects; determining, by aneural network, a pattern of voltage fluctuations characteristic of thefirst set of voltage fluctuation sequences and not characteristic of thesecond set voltage fluctuation sequences; providing a recommendation fora selection of subjects from among a third set of subjects based on thepattern of voltage fluctuations; and presenting, by the sensorypresentation device, a set of sequences of stimuli to the first set ofsubjects; determining, by the neural network, a respective correlationvalue for each of the set of sequences of stimuli; selecting, by thefirst computing device, the first sequence of stimuli from among the setof sequences of stimuli based on the respective correlation value forthe first sequence of stimuli.
 19. A system comprising: an electrode; acomputing device comprising a processor in communication with theelectrode; and a non-transitory, computer-readable medium incommunication with the processor and storing instructions that, whenexecuted by the processor, cause the processor to perform operationscomprising: presenting a first sequence of stimuli to: (i) a first setof subjects associated with a first selection criteria and (ii) a secondset of subjects associated with a second selection criteria; detecting afirst set of voltage fluctuation sequences, the first set of voltagefluctuation sequences comprising a sequence of voltage fluctuations fromeach of the first set of subjects; detecting a second set of voltagefluctuation sequences, the second set of voltage fluctuation sequencescomprising a sequence of voltage fluctuations from each of the secondset of subjects; determining, by a neural network, a pattern of voltagefluctuations characteristic of the first set of voltage fluctuationsequences and not characteristic of the second set voltage fluctuationsequences; providing a recommendation for a selection of subjects fromamong a third set of subjects based on the pattern of voltagefluctuations; presenting, by the sensory presentation device, a set ofsequences of stimuli to the first set of subjects; determining, by theneural network, a respective correlation value for each of the set ofsequences of stimuli; and selecting, by the first computing device, thefirst sequence of stimuli from among the set of sequences of stimulibased on the respective correlation value for the first sequence ofstimuli.